1. Field of the Invention
The present invention relates to an apparatus and method of estimating scale ratio and noise strength of an encoded image, and more particularly, to an apparatus and method of determining the position in which a noise reduction filter used to reduce noise, such as a block effect that occurs in an encoded image by performing a block-based image compression technique, is disposed and the strength of the noise reduction filter.
2. Description of the Related Art
In the field of visual communication, improvements in visual quality of an image and a video displayed on a large screen with high resolution are significant. As consumer devices such as digital cameras and video camcorders have been developed, users can easily upload their own clips on a web site, and display manufacturers have tried to display the clips on a relatively large screen.
Meanwhile, discrete cosine transform (DCT)-based coding scheme has been successfully used to compress still or moving images. Digital video contents are processed and encoded using various digital compression techniques so as to overcome a bandwidth limitation in a communication network. Such compressed digital videos include various artifacts that deteriorate the quality of moving pictures and scenes displayed on a screen. The artifacts in the compressed digital videos are referred to as compression noise. Compression noise reduction is an operation of detecting and removing this annoying JPEG or MPEG noise before digital videos are displayed on the screen. However, in most block reduction algorithms, a noise filtering operation is performed assuming that a block offset starts from a first pixel position of an input image. As a result, in the block reduction algorithms, when the block offset is varied, a block effect cannot be accurately reduced.
Many studies have been carried out to remove such artifacts, and they are classified into iterative and non-iterative methods. The iterative methods are based on the theory of projection onto convex sets (POCS) by iterating the projections onto a quantization constraint set and image smoothness constraint set until convergence is achieved. However, iteration-based methods usually impose high computational cost and are difficult to implement in real-time video or image processing.
Non-iterative methods include spatially adaptive filtering schemes that are relatively fast and have low bit rates. Furthermore, block discontinuity cannot be completely reduced using the spatially adaptive filtering schemes. Thus, overcomplete wavelet representation-based image-deblocking algorithms have been suggested. In overcomplete wavelet representation-based image-deblocking algorithms, multi-scale edge analysis can be performed so as to preserve the image details including edges. For example, discontinuity across the neighboring blocks is analyzed in the form of a known quantization step size, and a contaminated image is accurately filtered through several different wavelet scales. Most compression domain approaches that belong to the non-iterative scheme include filtering with DCT or other transforms by directly manipulating its coefficients in a transformed domain. A simple compressed-domain approach using useful information obtained from a decoder has recently shown a good result. Also, a training-based filtering without prior information has been suggested. However, the training-based filtering cannot be performed when an input image is much different from training data.
As described above, most of the approaches have emphasized post-processing using known information, such as a quantization step size. Thus, a technique for estimating noise strength without prior information is required so as to properly apply the deblocking algorithms.